Many attempts have been made to improve the scope, accuracy, compactness, efficiency and speed of image recognition and retrieval technologies that may be applied, for example, to implement large-scale digital image recognition searches. One focus of such attempts has been feature detection and description. At the most basic level, descriptors provide a means to characterize, summarize and index distinguishing features of an image (e.g., shapes, objects, etc.) for purposes of image recognition, search and retrieval. There are various methods for generating descriptors that represent the local features of an image. For example, the scale-invariant feature transform (SIFT), such as described in U.S. Pat. No. 6,711,293 to Lowe, is a currently popular image recognition algorithm used to detect and describe local features of images.
A global signature is a full image descriptor. One example of a global signature is a vector of locally aggregated descriptors (VLAD) built from local descriptors. In some instances, a global signature may be compressed or reduced in size (e.g., in relation to a sum of the local descriptors for an image) by further techniques to be a more compact method of describing images relative to large amounts of local descriptors. For example, one current technique for compressing global signatures is principal components analysis (PCA). Notably, the compression of global signatures can reduce the memory requirements necessary to practically operate an image recognition system.